Chevron WorkOrders

Marker clustering for performance
Interactive tracking and mapping
Analytics platform
Interactive maps
Technician registration

Inspiration

The project interested all of us because we have seen job scheduling algorithms in the real-world and wanted to take a stab at implementing them in the real world. We also wanted to stretch our knowledge of software engineering through learning to use various technologies such as MongoDB, Node.js, Twilio, React, Leaflet, and our brains ;)

What it does

Chevron WorkOrder is a full-stack service platform that tracks work orders that are submitted and the technicians that are completing them. Our platform optimizes assigned based on many factors, including knowing where technicians and facilities are, what they are certified to repair, how long they are planning to be there, other facility locations, as well as the work order priority.

In addition, we wanted to take it a step further and build out a full platform for Chevron that includes interactive SMS notifications for technicians to update their statuses, a real-time visualization of key business performance metrics, and a live-updating map to track worker and facility locations. We believe that these additional features, in companion with the original challenge specs, provide a superior analytics and work tracking experience for Chevron's users and technicians.

How we built it

We utilized Node.js with Express for the backend services, React for the frontend, and Twilio as a microservice for handling SMS flows. The optimization algorithm was implemented in Node.js using multivariate analysis and normalization.

Challenges we ran into

With so many features across the full stack that we wanted to accomplish, we had to learn many new technologies in order to implement them. We also ran into challenges with integrated all of our features into one consistent platform.

Accomplishments that we're proud of

We are proud that in less than 36 hours, we implemented more than what was required from the spec. Additionally, all our our data is live and none of the interactions with Chevron WorkOrder involve any hardcoded data.

What we learned

We learned best practices for working on and integrating features in parallel.

What's next for Chevron WorkOrders

We will continue to work on this and build out this full-fledged analytics platform.

Built With

Submitted to

HackRice 9
- Winner General Prize: 3rd Place

Created by

I worked on setting up the frontend and designing the optimized matching algorithm.

Kevin Lin
I built the SMS integration with Twilio.

Erica Zhong
I built out the backend infrastructure, set up our database, added the metrics and demo tabs along with their affiliated contents.

Anthony Cho
I worked on designing the optimization algorithm on the backend that efficiently schedules work orders for technicians. It was a good introductory lesson on NodeJS/React for me, as I have never been exposed to JavaScript before.

Paul Jiang